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- # coding: utf-8
- import typing
- from random import choice
-
- from sandbox.engulf.const import COLLECTION_GRASS, COLLECTION_CELL, COLLECTION_ALIVE, COLLECTION_PREY
- from sandbox.engulf.exceptions import NotFoundWhereToGo
- from synergine2.simulation import SubjectBehaviour, SimulationMechanism, SimulationBehaviour, SubjectBehaviourSelector
- from synergine2.simulation import Event
- from synergine2.utils import ChunkManager
- from synergine2.xyz import ProximitySubjectMechanism, DIRECTIONS, DIRECTION_SLIGHTLY, get_direction_from_north_degree
- from synergine2.xyz_utils import get_around_positions_of_positions, get_position_for_direction
-
- # Import for typing hint
- if False:
- from sandbox.engulf.subject import Grass
- from sandbox.engulf.subject import PreyCell
-
-
- class GrassGrownUp(Event):
- def __init__(self, subject_id, density, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self.subject_id = subject_id
- self.density = density
-
- def repr_debug(self) -> str:
- return '{}: subject_id:{}, density:{}'.format(
- self.__class__.__name__,
- self.subject_id,
- self.density,
- )
-
-
- class GrassSpawn(Event):
- def __init__(self, subject_id, position, density, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self.subject_id = subject_id
- self.position = position
- self.density = density
-
- def repr_debug(self) -> str:
- return '{}: subject_id:{}, position:{}, density:{}'.format(
- self.__class__.__name__,
- self.subject_id,
- self.position,
- self.density,
- )
-
-
- class GrassSpotablePositionsMechanism(SimulationMechanism):
- parallelizable = True
-
- def run(self, process_number: int=None, process_count: int=None):
- chunk_manager = ChunkManager(process_count)
- positions = list(self.simulation.subjects.grass_xyz.keys())
- positions_chunks = chunk_manager.make_chunks(positions)
- spotables = []
-
- for position in positions_chunks[process_number]:
- arounds = get_around_positions_of_positions(position)
- from_subject = self.simulation.subjects.grass_xyz[position][0]
- around_data = {
- 'from_subject': from_subject,
- 'around': [],
- }
- for around in arounds:
- if around not in self.simulation.subjects.grass_xyz and self.simulation.is_possible_position(around):
- around_data['around'].append(around)
-
- if around_data['around']:
- spotables.append(around_data)
-
- return spotables
-
-
- class GrowUp(SubjectBehaviour):
- frequency = 20
-
- def run(self, data):
- return True
-
- def action(self, data) -> [Event]:
- self.subject.density += 1
- return [GrassGrownUp(
- self.subject.id,
- self.subject.density,
- )]
-
-
- class GrassSpawnBehaviour(SimulationBehaviour):
- frequency = 100
- use = [GrassSpotablePositionsMechanism]
-
- def run(self, data):
- spawns = []
-
- for around_data in data[GrassSpotablePositionsMechanism]:
- from_subject = around_data['from_subject']
- arounds = around_data['around']
- if from_subject.density >= 40:
- spawns.extend(arounds)
-
- return spawns
-
- @classmethod
- def merge_data(cls, new_data, start_data=None):
- start_data = start_data or []
- start_data.extend(new_data)
- return start_data
-
- def action(self, data) -> [Event]:
- from sandbox.engulf.subject import Grass # cyclic
- events = []
-
- for position in data:
- if position not in list(self.simulation.subjects.grass_xyz.keys()):
- new_grass = Grass(
- config=self.config,
- simulation=self.simulation,
- position=position,
- density=20,
- )
- self.simulation.subjects.append(new_grass)
- events.append(GrassSpawn(
- new_grass.id,
- new_grass.position,
- new_grass.density,
- ))
-
- return events
-
-
- class GrassEatableDirectProximityMechanism(ProximitySubjectMechanism):
- distance = 1.41 # distance when on angle
- feel_collections = [COLLECTION_GRASS]
-
- def acceptable_subject(self, subject: 'Grass') -> bool:
- return subject.density >= self.config.simulation.eat_grass_required_density
-
-
- class PreyEatableDirectProximityMechanism(ProximitySubjectMechanism):
- distance = 1.41 # distance when on angle
- feel_collections = [COLLECTION_CELL]
-
- def acceptable_subject(self, subject: 'PreyCell') -> bool:
- # TODO: N'attaquer que des "herbivores" ?
- from sandbox.engulf.subject import PreyCell # cyclic
- return isinstance(subject, PreyCell)
-
-
- class MoveTo(Event):
- def __init__(self, subject_id: int, position: tuple, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self.subject_id = subject_id
- self.position = position
-
- def repr_debug(self) -> str:
- return '{}: subject_id:{}, position:{}'.format(
- type(self).__name__,
- self.subject_id,
- self.position,
- )
-
-
- class EatEvent(Event):
- def __init__(self, eaten_id: int, eater_id: int, eaten_new_density: float, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self.eaten_id = eaten_id
- self.eater_id = eater_id
- self.eaten_new_density = eaten_new_density
-
-
- class EatenEvent(Event):
- def __init__(self, eaten_id: int, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self.eaten_id = eaten_id
-
-
- class AttackEvent(Event):
- def __init__(self, attacker_id: int, attacked_id: int, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self.attacker_id = attacker_id
- self.attacked_id = attacked_id
-
-
- class SearchFood(SubjectBehaviour):
- mechanism_data_class = NotImplemented
-
- def get_required_appetite(self) -> float:
- raise NotImplementedError()
-
- def run(self, data):
- if self.subject.appetite < self.get_required_appetite():
- return False
-
- if not data[self.mechanism_data_class]:
- return False
-
- direction_degrees = [d['direction'] for d in data[self.mechanism_data_class]]
- return get_direction_from_north_degree(choice(direction_degrees))
-
- def action(self, data) -> [Event]:
- direction = data
- position = get_position_for_direction(self.subject.position, direction)
- self.subject.position = position
- self.subject.previous_direction = direction
-
- return [MoveTo(self.subject.id, position)]
-
-
- class SearchGrass(SearchFood):
- """
- Si une nourriture a une case de distance et cellule non rassasié, move dans sa direction.
- """
- use = [GrassEatableDirectProximityMechanism]
- mechanism_data_class = use[0]
-
- def get_required_appetite(self) -> float:
- return self.config.simulation.search_food_appetite_required
-
-
- class SearchPrey(SearchFood):
- """
- Si une nourriture a une case de distance et cellule non rassasié, move dans sa direction.
- """
- use = [PreyEatableDirectProximityMechanism]
- mechanism_data_class = use[0]
-
- def get_required_appetite(self) -> float:
- return self.config.simulation.search_and_attack_prey_apetite_required
-
-
- class EatGrass(SubjectBehaviour):
- def run(self, data):
- if self.subject.appetite < self.config.simulation.eat_grass_required_density:
- return False
-
- for grass in self.simulation.collections.get(COLLECTION_GRASS, []):
- # TODO: Use simulation/xyz pre calculated indexes
- if grass.position == self.subject.position:
- return grass.id
-
- def action(self, data) -> [Event]:
- subject_id = data
-
- grass = self.simulation.subjects.index[subject_id] # TODO: cas ou grass disparu ?
- grass.density -= self.config.simulation.eat_grass_density_reduction
-
- self.subject.appetite -= self.config.simulation.eat_grass_appetite_reduction
-
- # TODO: Comment mettre des logs (ne peuvent pas être passé aux subprocess)?
-
- return [EatEvent(
- eater_id=self.subject.id,
- eaten_id=subject_id,
- eaten_new_density=grass.density,
- )]
-
-
- class EatPrey(SubjectBehaviour):
- def run(self, data):
- if self.subject.appetite < self.config.simulation.search_and_attack_prey_apetite_required:
- return False
-
- for prey in self.simulation.collections.get(COLLECTION_PREY, []):
- # TODO: Use simulation/xyz pre calculated indexes
- if prey.position == self.subject.position:
- return prey.id
-
- def action(self, data) -> [Event]:
- subject_id = data
-
- # Cell already eaten ?
- if subject_id not in self.simulation.subjects.index:
- return []
-
- prey = self.simulation.subjects.index[subject_id]
- self.simulation.subjects.remove(prey)
- self.subject.appetite -= self.config.simulation.eat_prey_required_density
-
- return [EatenEvent(
- eaten_id=prey.id,
- )]
-
-
- class Explore(SubjectBehaviour):
- """
- Produit un mouvement au hasard (ou un immobilisme)
- """
- use = []
-
- def run(self, data):
- # TODO: Il faut pouvoir dire tel behaviour concerne que tel collections
- if COLLECTION_ALIVE not in self.subject.collections:
- return False
-
- return True
-
- def action(self, data) -> [Event]:
- try:
- position, direction = self.get_random_position_and_direction()
- self.subject.position = position
- self.subject.previous_direction = direction
- return [MoveTo(self.subject.id, position)]
- except NotFoundWhereToGo:
- return []
-
- def get_random_position_and_direction(self) -> tuple:
- attempts = 0
-
- while attempts <= 5:
- attempts += 1
- direction = self.get_random_direction()
- position = get_position_for_direction(self.subject.position, direction)
-
- if self.simulation.is_possible_subject_position(self.subject, position):
- return position, direction
-
- # If blocked, permit any direction (not slightly)
- if attempts >= 3:
- self.subject.previous_direction = None
-
- raise NotFoundWhereToGo()
-
- def get_random_direction(self):
- if not self.subject.previous_direction:
- return choice(DIRECTIONS)
- return choice(DIRECTION_SLIGHTLY[self.subject.previous_direction])
-
-
- class Hungry(SubjectBehaviour):
- def run(self, data):
- return True
-
- def action(self, data) -> [Event]:
- self.subject.appetite += self.config.simulation.hungry_reduction
- return []
-
- def get_random_direction(self):
- if not self.subject.previous_direction:
- return choice(DIRECTIONS)
- return choice(DIRECTION_SLIGHTLY[self.subject.previous_direction])
-
-
- class Attack(SubjectBehaviour):
- use = [PreyEatableDirectProximityMechanism]
-
- def run(self, data):
- if self.subject.appetite < self.config.simulation.search_and_attack_prey_apetite_required:
- return False
-
- eatable_datas = data[PreyEatableDirectProximityMechanism]
- attackable_datas = []
-
- for eatable_data in eatable_datas:
- if COLLECTION_ALIVE in eatable_data['subject'].collections:
- attackable_datas.append(eatable_data)
-
- if attackable_datas:
- return choice(attackable_datas)
- return False
-
- def action(self, data) -> [Event]:
- attacked = self.simulation.subjects.index[data['subject'].id]
-
- try:
- # TODO il faut automatiser/Refactoriser le fait de retirer/ajouter un collection
- self.simulation.collections[COLLECTION_ALIVE].remove(attacked)
- attacked.collections.remove(COLLECTION_ALIVE)
- except ValueError:
- pass # On considere qu'il a ete tué par une autre, TODO: en être sur ?
-
- return [AttackEvent(attacker_id=self.subject.id, attacked_id=data['subject'].id)]
-
-
- class CellBehaviourSelector(SubjectBehaviourSelector):
- # If behaviour in sublist, only one be kept in sublist
- behaviour_hierarchy = ( # TODO: refact it
- (
- EatPrey,
- EatGrass, # TODO: Introduce priority with appetite
- Attack,
- SearchPrey,
- SearchGrass, # TODO: Introduce priority with appetite
- Explore,
- ),
- )
-
- def reduce_behaviours(
- self,
- behaviours: typing.Dict[typing.Type[SubjectBehaviour], object],
- ) -> typing.Dict[typing.Type[SubjectBehaviour], object]:
- reduced_behaviours = {} # type: typing.Dict[typing.Type[SubjectBehaviour], object]
-
- for behaviour_class, behaviour_data in behaviours.items():
- if not self.behaviour_class_in_sublist(behaviour_class):
- reduced_behaviours[behaviour_class] = behaviour_data
- elif self.behaviour_class_is_prior(behaviour_class, behaviours):
- reduced_behaviours[behaviour_class] = behaviour_data
-
- return reduced_behaviours
-
- def behaviour_class_in_sublist(self, behaviour_class: typing.Type[SubjectBehaviour]) -> bool:
- for sublist in self.behaviour_hierarchy:
- if behaviour_class in sublist:
- return True
- return False
-
- def behaviour_class_is_prior(
- self,
- behaviour_class: typing.Type[SubjectBehaviour],
- behaviours: typing.Dict[typing.Type[SubjectBehaviour], object],
- ) -> bool:
- for sublist in self.behaviour_hierarchy:
- if behaviour_class in sublist:
- behaviour_position = sublist.index(behaviour_class)
- other_behaviour_top_position = self.get_other_behaviour_top_position(
- behaviour_class,
- behaviours,
- sublist,
- )
- if other_behaviour_top_position is not None and behaviour_position > other_behaviour_top_position:
- return False
- return True
-
- def get_other_behaviour_top_position(
- self,
- exclude_behaviour_class,
- behaviours,
- sublist,
- ) -> typing.Union[None, int]:
- position = None
- for behaviour_class in behaviours.keys():
- if behaviour_class != exclude_behaviour_class:
- try:
- behaviour_position = sublist.index(behaviour_class)
- if position is None or behaviour_position < position:
- position = behaviour_position
- except ValueError:
- pass
-
- return position
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